# High Precision Audience Expansion via Extreme Classification in a Two-Sided Marketplace

arXiv: https://arxiv.org/abs/2602.14358 · HTML: https://mlsanity.com/paper/2602.14358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [On Using Very Large Target Vocabulary for Neural Machine Translation](https://mlsanity.com/paper/1412.2007/) (2014)
- [Improving Deep Learning For Airbnb Search](https://mlsanity.com/paper/2002.05515/) (2020)
- [Managing Diversity in Airbnb Search](https://mlsanity.com/paper/2004.02621/) (2020)
- [Learning To Rank Diversely At Airbnb](https://mlsanity.com/paper/2210.07774/) (2022)

## Abstract (arXiv)

Airbnb search must balance a worldwide, highly varied supply of homes with guests whose location, amenity, style, and price expectations differ widely. Meeting those expectations hinges on an efficient retrieval stage that surfaces only the listings a guest might realistically book, before resource intensive ranking models are applied to determine the best results. Unlike many recommendation engines, our system faces a distinctive challenge, location retrieval, that sits upstream of ranking and determines which geographic areas are queried in order to filter inventory to a candidate set. The preexisting approach employs a deep bayesian bandit based system to predict a rectangular retrieval bounds area that can be used for filtering. The purpose of this paper is to demonstrate the methodology, challenges, and impact of rearchitecting search to retrieve from the subset of most bookable high precision rectangular map cells defined by dividing the world into 25M uniform cells.